Can machine learning aid in delivering new use cases and scenarios in 5G?

Teodora Sandra Buda; Haytham Assem; Lei Xu; Danny Raz; Udi Margolin; Elisha Rosensweig; Diego R. Lopez; Marius Iulian Corici; Mikhail Smirnov; Robert Mullins; Olga Uryupina; Alberto Mozo; Bruno Ordozgoiti; Angel Martin; Alaa Alloush; Pat O'Sullivan; Imen Grida Ben Yahia

doi:10.1109/NOMS.2016.7503003

Can machine learning aid in delivering new use cases and scenarios in 5G?

Teodora Sandra Buda, Haytham Assem, Lei Xu, Danny Raz, Udi Margolin, Elisha Rosensweig, Diego R. Lopez, Marius Iulian Corici, Mikhail Smirnov, Robert Mullins, Olga Uryupina, Alberto Mozo, Bruno Ordozgoiti, Angel Martin, Alaa Alloush, Pat O'Sullivan, Imen Grida Ben Yahia

Department of Computing and Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

26 Citations (Scopus)

Abstract

5G represents the next generation of communication networks and services, and will bring a new set of use cases and scenarios. These in turn will address a new set of challenges from the network and service management perspective, such as network traffic and resource management, big data management and energy efficiency. Consequently, novel techniques and strategies are required to address these challenges in a smarter way. In this paper, we present the limitations of the current network and service management and describe in detail the challenges that 5G is expected to face from a management perspective. The main contribution of this paper is presenting a set of use cases and scenarios of 5G in which machine learning can aid in addressing their management challenges. It is expected that machine learning can provide a higher and more intelligent level of monitoring and management of networks and applications, improve operational efficiencies and facilitate the requirements of the future 5G network.

Original language	English
Title of host publication	Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium
Editors	Sema Oktug Badonnel, Mehmet Ulema, Cicek Cavdar, Lisandro Zambenedetti Granville, Carlos Raniery P. dos Santos
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1279-1284
Number of pages	6
ISBN (Electronic)	9781509002238
DOIs	https://doi.org/10.1109/NOMS.2016.7503003
Publication status	Published - 30 Jun 2016
Event	2016 IEEE/IFIP Network Operations and Management Symposium, NOMS 2016 - Istanbul, Turkey Duration: 25 Apr 2016 → 29 Apr 2016

Publication series

Name	Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium

Conference

Conference	2016 IEEE/IFIP Network Operations and Management Symposium, NOMS 2016
Country/Territory	Turkey
City	Istanbul
Period	25/04/2016 → 29/04/2016

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/NOMS.2016.7503003

Cite this

Buda, T. S., Assem, H., Xu, L., Raz, D., Margolin, U., Rosensweig, E., Lopez, D. R., Corici, M. I., Smirnov, M., Mullins, R., Uryupina, O., Mozo, A., Ordozgoiti, B., Martin, A., Alloush, A., O'Sullivan, P., & Ben Yahia, I. G. (2016). Can machine learning aid in delivering new use cases and scenarios in 5G? In S. O. Badonnel, M. Ulema, C. Cavdar, L. Z. Granville, & C. R. P. dos Santos (Eds.), Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium (pp. 1279-1284). [7503003] (Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NOMS.2016.7503003

Buda, Teodora Sandra ; Assem, Haytham ; Xu, Lei ; Raz, Danny ; Margolin, Udi ; Rosensweig, Elisha ; Lopez, Diego R. ; Corici, Marius Iulian ; Smirnov, Mikhail ; Mullins, Robert ; Uryupina, Olga ; Mozo, Alberto ; Ordozgoiti, Bruno ; Martin, Angel ; Alloush, Alaa ; O'Sullivan, Pat ; Ben Yahia, Imen Grida. / Can machine learning aid in delivering new use cases and scenarios in 5G?. Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium. editor / Sema Oktug Badonnel ; Mehmet Ulema ; Cicek Cavdar ; Lisandro Zambenedetti Granville ; Carlos Raniery P. dos Santos. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1279-1284 (Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium).

@inproceedings{add472e52ed54f22afed4154cdb87241,

title = "Can machine learning aid in delivering new use cases and scenarios in 5G?",

abstract = "5G represents the next generation of communication networks and services, and will bring a new set of use cases and scenarios. These in turn will address a new set of challenges from the network and service management perspective, such as network traffic and resource management, big data management and energy efficiency. Consequently, novel techniques and strategies are required to address these challenges in a smarter way. In this paper, we present the limitations of the current network and service management and describe in detail the challenges that 5G is expected to face from a management perspective. The main contribution of this paper is presenting a set of use cases and scenarios of 5G in which machine learning can aid in addressing their management challenges. It is expected that machine learning can provide a higher and more intelligent level of monitoring and management of networks and applications, improve operational efficiencies and facilitate the requirements of the future 5G network.",

author = "Buda, {Teodora Sandra} and Haytham Assem and Lei Xu and Danny Raz and Udi Margolin and Elisha Rosensweig and Lopez, {Diego R.} and Corici, {Marius Iulian} and Mikhail Smirnov and Robert Mullins and Olga Uryupina and Alberto Mozo and Bruno Ordozgoiti and Angel Martin and Alaa Alloush and Pat O'Sullivan and {Ben Yahia}, {Imen Grida}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; null ; Conference date: 25-04-2016 Through 29-04-2016",

year = "2016",

month = jun,

day = "30",

doi = "10.1109/NOMS.2016.7503003",

language = "English",

series = "Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1279--1284",

editor = "Badonnel, {Sema Oktug} and Mehmet Ulema and Cicek Cavdar and Granville, {Lisandro Zambenedetti} and {dos Santos}, {Carlos Raniery P.}",

booktitle = "Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium",

address = "United States",

}

Buda, TS, Assem, H, Xu, L, Raz, D, Margolin, U, Rosensweig, E, Lopez, DR, Corici, MI, Smirnov, M, Mullins, R, Uryupina, O, Mozo, A, Ordozgoiti, B, Martin, A, Alloush, A, O'Sullivan, P & Ben Yahia, IG 2016, Can machine learning aid in delivering new use cases and scenarios in 5G? in SO Badonnel, M Ulema, C Cavdar, LZ Granville & CRP dos Santos (eds), Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium., 7503003, Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium, Institute of Electrical and Electronics Engineers Inc., pp. 1279-1284, 2016 IEEE/IFIP Network Operations and Management Symposium, NOMS 2016, Istanbul, Turkey, 25/04/2016. https://doi.org/10.1109/NOMS.2016.7503003

Can machine learning aid in delivering new use cases and scenarios in 5G? / Buda, Teodora Sandra; Assem, Haytham; Xu, Lei; Raz, Danny; Margolin, Udi; Rosensweig, Elisha; Lopez, Diego R.; Corici, Marius Iulian; Smirnov, Mikhail; Mullins, Robert; Uryupina, Olga; Mozo, Alberto; Ordozgoiti, Bruno; Martin, Angel; Alloush, Alaa; O'Sullivan, Pat; Ben Yahia, Imen Grida.

Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium. ed. / Sema Oktug Badonnel; Mehmet Ulema; Cicek Cavdar; Lisandro Zambenedetti Granville; Carlos Raniery P. dos Santos. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1279-1284 7503003 (Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review